Aiming to address the problem of insufficient optimization of energy consumption in unmanned aerial vehicle (UAV) path planning, a three-dimensional (3D) path planning method based on a tangent map and considering energy consumption is proposed.

First, to ensure safe UAV flight, an ellipsoidal obstacle modeling approach is introduced. This approach represents irregular obstacles using a safety envelope, ensuring a minimum safe distance between the UAV and obstacles. Unlike conventional envelope-based methods, the proposed approach eliminates path redundancy, thereby lowering computational complexity and enhancing planning efficiency and flight safety. Second, the traditional elliptic tangent graph method is improved by incorporating a bidirectional search strategy and a node screening mechanism. These enhancements generate optimized two-dimensional (2D) reference path points, notably reducing the number of turning points along the path and shortening the overall path length. Finally, the proposed method integrates the 2D reference path points with an energy consumption model to enable 3D path planning. The 3D reference path points are derived from their 2D counterparts. When the start and end points of the UAV lie at the same altitude, a dimensionality reduction strategy is applied to convert the 3D planning problem into a 2D planar one, which is then solved using the elliptic tangent graph method. In cases involving height differences between the start and end points, an energy evaluation model is used to compare the energy costs of two strategies (horizontal flyover and vertical climb). The path point with the lowest energy consumption is selected, and cubic B-spline curves are applied to smooth the path. Aiming to evaluate the performance of the proposed method, three test scenarios with varying obstacle densities and layouts are designed. Comparative experiments are conducted against four benchmark algorithms: A^*, rapidly-exploring random trees (RRT), particle swarm optimization (PSO), and the vector field histogram (VFH).

Results demonstrate that, in 2D environments, the improved elliptic tangent graph method consistently generates the shortest paths with the fewest turns, regardless of obstacle distribution. Its performance advantage becomes increasingly evident as environmental complexity rises. In complex 3D environments, the method not only delivers shorter and smoother flight paths but also substantially reduces the overall energy consumption of UAV operations. Specifically, compared with the A^*, RRT, PSO, and VFH algorithms, the proposed method achieves average reductions in path length of 8.7%, 18.7%, 13.4%, and 4.1%, respectively; reductions in the number of turns of 68.8%, 82.1%, 82.8%, and 75.0%; and reductions in energy consumption of 51.6%, 34.0%, 59.1%, and 55.3%. Additionally, comparative experiments conducted with varying safety distances (2, 4, and 6 m) reveal that appropriately increasing the safety distance can improve flight safety without compromising path optimality. However, excessively large safety distances may lead to inefficient use of free space and reduced planning efficiency.

These improvements effectively overcome the traditional tradeoffs between path length, motion smoothness, and energy efficiency, offering a solution that combines theoretical innovation with engineering practicality to enhance UAV mission endurance and operational safety.

This study focuses on the critical task of predicting the number of people to be evacuated (i.e., relocation number) during flood disasters. Accurate predictions of relocation numbers are vital for ensuring timely resource allocation and efficient disaster management, particularly in flood-prone areas where rapid decision-making can drastically mitigate the adverse impacts of the disaster.

This research developed a robust relocation number prediction framework that combines feature selection and data augmentation techniques using the extreme gradient boosting (XGBoost) model, a widely used gradient-boosting machine learning algorithm. The model was built using historical data from flood events across China between 2014 and 2018. These events included meteorological and geographical features and the relocation number during each disaster. Feature selection was accomplished using Shapley additive explanations (SHAP), a game theory method for measuring the contribution of each feature to the model predictions. The selected features were then fed into the XGBoost model for training. A data augmentation strategy was also introduced to handle the challenge of limited training samples. This strategy involved the injection of Gaussian noise using a weighted k-nearest neighbors method to generate synthetic data points that preserved the local structure of the data, thereby enhancing the model's robustness and generalization ability.

The study demonstrates that the XGBoost model performs well with the selected features and augmented data. Initially, the model is trained on a small dataset, leading to satisfactory accuracy but limited generalization ability. However, after applying data augmentation, the model's performance significantly improves, especially for extreme values in the data. The testing phase reveals that R² improves from 0.854 to 0.967, indicating a substantial increase in the model's predictive accuracy. Additionally, the root mean square error decreases from 0.296 to 0.123, signifying a considerable reduction in prediction error. These results highlight the effectiveness of combining feature selection and data augmentation to enhance the predictive power of the model. The feature selection process, guided by SHAP, identifies several key predictors that play a dominant role in determining population relocation demand. Among the most influential features are the maximum 3-day cumulative rainfall (MCR) and the maximum cumulative rainfall over the 15 days prior to the event (MRPE). These features are the most important in predicting the relocation number during flood events.

The proposed relocation number prediction framework, integrating feature selection through SHAP and data augmentation techniques, is a highly effective tool for forecasting the relocation number during flood disasters. The XGBoost model, after optimization through Bayesian hyperparameter tuning and data augmentation, demonstrates significantly improved prediction accuracy and robustness. This approach can be instrumental in supporting disaster management teams with more reliable forecasts, allowing for better planning and more timely deployment of resources. Improving the model's ability to generalize to unseen data ensures accurate predictions even in regions with limited historical data. Thus, this study provides a valuable decision-making support tool for emergency response teams, helping to streamline resource allocation and evacuation planning during flood disasters and thereby minimizing the impact of the disaster on human lives and infrastructure.

With increased globalization, multiple countries are involved in supply chains, forming complex supply networks. Frequent occurrences of natural disasters, geopolitical instability, and global health crises pose unprecedented challenges to traditional supply chain management methods. Local disruptions in the supply chain can spread internally, causing a series of chain reactions. Enhancing supply chain risk resilience and robustness has become a research focus for many scholars. The widespread use of the Internet has led to rapid information exchange between enterprises; an increasing number of scholars have recognized the importance of early warning information in preventing supply chain disruptions. Therefore, understanding how information affects the propagation of risks within the supply chain and maximizing the early warning function of information have significant practical implications. Moreover, the heterogeneity in the responses of enterprises to early warning information also needs attention.

To capture the propagation of early warning information and disruption risks, a two-layer propagation model that couples risk and information is constructed. In this model, the upper layer represents the information layer and the lower layer represents the risk layer. The information of a disruption in a lower-layer enterprise is transmitted to upstream and downstream enterprises with a certain probability. After receiving the early warning information, an enterprise transitions into a conscious node and this transition is reflected in the upper layer network. In this model, there are five possible states for the nodes in the network. A microscopic Markov chain (MMC) method is used to analyze the state transition process between nodes and calculate the risk propagation threshold of the system. Furthermore, the key factors influencing the propagation of disruption risk are analyzed. An agent-based approach is used for case simulation to validate the model's effectiveness. Numerical analysis of the model reveals that the network structure, network size, extent of risk information propagation in the information layer, and the probability of disruption risk propagation are the key factors influencing the propagation of the risk. Financial data from Tesla's supply chain in China are also collected. In case simulation, an agent-based method is used to study the effects of the information layer network structure, information propagation rate, and risk propagation rate on the supply chain resilience.

The results show that for a low information propagation rate, the scale-free network structure accelerates information dissemination, allowing more enterprises to quickly obtain early warning information, thereby helping the supply chain resist risks and improve resilience. When the information propagation rate exceeds 0.4, the small-world network structures can propagate risks more efficiently because of their shorter average paths. Additionally, three disruption schemes are used to analyze system resilience, revealing that prioritizing the disruption of nodes with higher degrees has the greatest impact on the network, while deliberately attacking nodes with smaller degrees allows the supply chain to maintain higher operational efficiency. This finding suggests that maintaining the robustness of the key nodes in the supply chain is critical for enhancing the overall network resilience.

Adjusting the supply chain network structure can help improve the risk resilience and robustness of the system. Enhancing risk awareness of enterprises and their response strategies can effectively improve supply chain resilience and suppress risk diffusion. Deliberate attacks on hub nodes with high degrees cause the greatest damage to the network system. Thus, this study provides theoretical support for supply chain management and can serve as a basis for decision-making to improve supply chain risk resilience and optimize management strategies.

Safe and effective evacuation of individuals during extreme weather conditions is critical in evacuation planning. Compared with traditional personnel evacuation and emergency transportation studies, evacuation zone planning is in its early stages. It lacks comprehensive consideration of major urban disaster scenarios, particularly beyond hurricanes. Additionally, there is no unified system for defining problems or measuring urban population hotspots, spatiotemporal disaster impacts, and exit distribution in evacuation planning. To address the practical issues of evacuating affected individuals during heavy rain and flood disasters, this paper proposes a model for delineating evacuation zones.

Starting with establishing evacuation needs and quantifying the impact of disasters on road segments, this study considers urban population distribution hotspots and the characteristics of heavy rain and flood disasters. Through modeling analysis, geographic information system (GIS) visualization, and other methods, a model is developed for the integrated delineation of evacuation zones and the allocation of evacuees at exits. The main components include the following: (1) To identify hotspot areas in urban functional zones and establish evacuation needs based on the city's road network. (2) To assess the risk of heavy rain and flood disasters, establish a risk indicator system for flood risk assessment (including causative factors, disaster-prone environments, disaster-prone bodies, and disaster prevention and mitigation capabilities), and develop a road damage model to determine road network damage. (3) To construct a two-tier planning optimization model to determine evacuation paths and exit allocations. (4) To use Wuhan's Wuchang district as an example, the effectiveness of the proposed method for large-scale urban evacuation zone planning under flood disasters is validated. The upper-level model provides the proportion of evacuees that each evacuation point should accommodate, with these allocation ratios stored in chromosomes as input for the lower level. The lower-level problem uses the incoming allocation ratios to calculate the evacuation flow for each OD pair and evaluates the fitness of the upper-level chromosomes. This is achieved using the Frank-Wolfe algorithm. The two-tier framework allows for detailed treatment of complex evacuation planning problems, ensuring the global minimization of total evacuation time and individual minimization of evacuee travel time.

The innovative aspects included identifying evacuation needs in urban hotspots and constructing road damage levels under risk zoning for heavy rain and flood disasters. The two-tier planning optimization model minimized overall evacuation time and individual travel time, making the evacuation plan more realistic and reasonable.

The proposed method for large-scale urban evacuation zone planning is feasible, risk assessment is essential in actual evacuation planning. Significant differences exist in day and night population distribution with daytime populations primarily concentrated in commercial and work areas and nighttime populations concentrated in residential areas. Emergency management departments should develop varied evacuation plans for different periods. Due to potential road damage during disasters, preplan alternative evacuation routes and make real-time dynamic adjustments during evacuations.